Alzoubi Isham, Delavar Mahmoud R, Mirzaei Farhad, Arrabi Babak Nadjar
1Department of Surveying and Geometric Engineering, Engineering Faculty, University of Tehran, Tehran, Iran.
2College of Agriculture and Natural Resources, University of Tehran, Tehran, Iran.
J Environ Health Sci Eng. 2018 Apr 11;16(1):65-80. doi: 10.1007/s40201-018-0297-3. eCollection 2018 Jun.
Land leveling is one of the most important steps in soil preparation and cultivation. Although land leveling with machines require considerable amount of energy, it delivers a suitable surface slope with minimal deterioration of the soil and damage to plants and other organisms in the soil. Notwithstanding, researchers during recent years have tried to reduce fossil fuel consumption and its deleterious side effects. The aim of this work was to determine best linear model using artificial neural network (ANN), imperialist competitive algorithm and ANN and regression and adaptive neural fuzzy inference system (ANFIS) in order to predict the environmental indicators for land leveling.
New techniques such as; ANN, imperialist competitive algorithm and ANN and sensitivity analysis and regression and ANFIS that will lead to a noticeable improvement in the environment. In this research effects of various soil properties such as embankment volume, soil compressibility factor, specific gravity, moisture content, slope, sand percent, and soil swelling index in energy consumption were investigated. The study was consisted of 90 samples were collected from 3 different regions. The grid size was set 20 m in 20 m (20 × 20) from a farmland in Karaj province of Iran.
According to the results of sensitivity analysis, only three parameters; density, soil compressibility factor and, embankment volume index had significant effect on fuel consumption. In comparison with ANN, all ICA-ANN models had higher accuracy in prediction according to their higher R value and lower RMSE value. Statistical factors of RMSE and R illustrate the superiority of ICA-ANN over other methods by values about 0.02 and 0.99, respectively.
Results extracted and statistical analysis was performed and RMSE as well as coefficient of determination, R, of the models were determined as a criterion to compare selected models. According to the results, 10-8-3-1, 10-8-2-5-1, 10-5-8-10-1, and 10-6-4-1 MLP network structures were chosen as the best arrangements and were trained using Levenberg-Marquet as NTF. Integrating ANN and imperialist competitive algorithm (ICA-ANN) had better performance in prediction of output parameters in comparison with conventional methods such.
土地平整是土壤准备和耕作中最重要的步骤之一。尽管使用机器进行土地平整需要消耗大量能源，但它能提供合适的地表坡度，同时使土壤退化、对植物和土壤中的其他生物造成的损害最小化。尽管如此，近年来研究人员一直在努力减少化石燃料的消耗及其有害副作用。这项工作的目的是使用人工神经网络（ANN）、帝国主义竞争算法与ANN以及回归和自适应神经模糊推理系统（ANFIS）来确定最佳线性模型，以便预测土地平整的环境指标。
采用诸如ANN、帝国主义竞争算法与ANN以及敏感性分析和回归与ANFIS等新技术，这些技术将显著改善环境。在本研究中，调查了各种土壤特性，如堤岸体积、土壤压缩系数、比重、含水量、坡度、砂含量和土壤膨胀指数对能源消耗的影响。该研究由从3个不同地区收集的90个样本组成。网格大小设定为伊朗卡拉季省一块农田中20米×20米（20 × 20）。
根据敏感性分析结果，只有三个参数，即密度、土壤压缩系数和堤岸体积指数对燃料消耗有显著影响。与ANN相比，所有ICA - ANN模型根据其较高的R值和较低的RMSE值在预测方面具有更高的准确性。RMSE和R的统计因子分别以约0.02和0.99的值说明了ICA - ANN相对于其他方法的优越性。
提取结果并进行统计分析，将模型的RMSE以及决定系数R确定为比较所选模型的标准。根据结果，选择10 - 8 - 3 - 1、10 - 8 - 2 - 5 - 1、10 - 5 - 8 - 10 - 1和10 - 6 - 4 - 1的MLP网络结构作为最佳配置，并使用Levenberg - Marquet作为NTF进行训练。与传统方法相比，将ANN与帝国主义竞争算法（ICA - ANN）相结合在输出参数预测方面具有更好的性能。
